Python torch.IntTensor方法代码示例

# 需要导入模块: import torch [as 别名]
# 或者: from torch import IntTensor [as 别名]
def ctc_collate(batch):
    '''
    Stack samples into CTC style inputs.
    Modified based on default_collate() in PyTorch.
    By Yuan-Hang Zhang.
    '''
    xs, ys, lens, indices = zip(*batch)
    max_len = max(lens)
    x = default_collate(xs)
    x.narrow(2, 0, max_len)
    y = []
    for sub in ys: y += sub
    y = torch.IntTensor(y)
    lengths = torch.IntTensor(lens)
    y_lengths = torch.IntTensor([len(label) for label in ys])
    ids = default_collate(indices)

    return x, y, lengths, y_lengths, ids 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import IntTensor [as 别名]
def forward(self, features, rois):
        batch_size, num_channels, data_height, data_width = features.size()
        num_rois = rois.size()[0]
        output = torch.zeros(num_rois, num_channels, self.pooled_height, self.pooled_width)
        argmax = torch.IntTensor(num_rois, num_channels, self.pooled_height, self.pooled_width).zero_()

        if not features.is_cuda:
            _features = features.permute(0, 2, 3, 1)
            roi_pooling.roi_pooling_forward(self.pooled_height, self.pooled_width, self.spatial_scale,
                                            _features, rois, output)
            # output = output.cuda()
        else:
            output = output.cuda()
            argmax = argmax.cuda()
            roi_pooling.roi_pooling_forward_cuda(self.pooled_height, self.pooled_width, self.spatial_scale,
                                                 features, rois, output, argmax)
            self.output = output
            self.argmax = argmax
            self.rois = rois
            self.feature_size = features.size()

        return output 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import IntTensor [as 别名]
def tensorize(mol_batch, vocab, avocab):
        mol_batch = [MolGraph(x) for x in mol_batch]
        tree_tensors, tree_batchG = MolGraph.tensorize_graph([x.mol_tree for x in mol_batch], vocab)
        graph_tensors, graph_batchG = MolGraph.tensorize_graph([x.mol_graph for x in mol_batch], avocab)
        tree_scope = tree_tensors[-1]
        graph_scope = graph_tensors[-1]

        max_cls_size = max( [len(c) for x in mol_batch for c in x.clusters] )
        cgraph = torch.zeros(len(tree_batchG) + 1, max_cls_size).int()
        for v,attr in tree_batchG.nodes(data=True):
            bid = attr['batch_id']
            offset = graph_scope[bid][0]
            tree_batchG.nodes[v]['inter_label'] = inter_label = [(x + offset, y) for x,y in attr['inter_label']]
            tree_batchG.nodes[v]['cluster'] = cls = [x + offset for x in attr['cluster']]
            tree_batchG.nodes[v]['assm_cands'] = [add(x, offset) for x in attr['assm_cands']]
            cgraph[v, :len(cls)] = torch.IntTensor(cls)

        all_orders = []
        for i,hmol in enumerate(mol_batch):
            offset = tree_scope[i][0]
            order = [(x + offset, y + offset, z) for x,y,z in hmol.order[:-1]] + [(hmol.order[-1][0] + offset, None, 0)]
            all_orders.append(order)

        tree_tensors = tree_tensors[:4] + (cgraph, tree_scope)
        return (tree_batchG, graph_batchG), (tree_tensors, graph_tensors), all_orders 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import IntTensor [as 别名]
def predict_this_box(image, model, alphabet):
    converter = utils.strLabelConverter(alphabet)
    transformer = dataset.resizeNormalize((200, 32))
    image = transformer(image)
    if torch.cuda.is_available():
        image = image.cuda()
    image = image.view(1, *image.size())
    image = Variable(image)

    model.eval()
    preds = model(image)

    _, preds = preds.max(2)
    preds = preds.transpose(1, 0).contiguous().view(-1)

    preds_size = Variable(torch.IntTensor([preds.size(0)]))
    raw_pred = converter.decode(preds.data, preds_size.data, raw=True)
    sim_pred = converter.decode(preds.data, preds_size.data, raw=False)
    print('%-30s => %-30s' % (raw_pred, sim_pred))
    return sim_pred 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import IntTensor [as 别名]
def encode(self, text):
        """Support batch or single str.

        Args:
            text (str or list of str): texts to convert.

        Returns:
            torch.IntTensor [length_0 + length_1 + ... length_{n - 1}]: encoded texts.
            torch.IntTensor [n]: length of each text.
        """
        if isinstance(text, str):
            text = [
                self.dict[char.lower() if self._ignore_case else char]
                for char in text
            ]
            length = [len(text)]
        elif isinstance(text, collections.Iterable):
            length = [len(s) for s in text]
            text = ''.join(text)
            text, _ = self.encode(text)
        return (torch.IntTensor(text), torch.IntTensor(length)) 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import IntTensor [as 别名]
def trainBatch(net, criterion, optimizer):
    data = train_iter.next()
    cpu_images, cpu_texts = data
    batch_size = cpu_images.size(0)
    utils.loadData(image, cpu_images)
    t, l = converter.encode(cpu_texts)
    utils.loadData(text, t)
    utils.loadData(length, l)

    preds = crnn(image)
    preds_size = Variable(torch.IntTensor([preds.size(0)] * batch_size))
    cost = criterion(preds, text, preds_size, length) / batch_size
    crnn.zero_grad()
    cost.backward()
    optimizer.step()
    return cost 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import IntTensor [as 别名]
def cast_type(var, dtype, use_gpu):
    if use_gpu:
        if dtype == INT:
            var = var.type(th.cuda.IntTensor)
        elif dtype == LONG:
            var = var.type(th.cuda.LongTensor)
        elif dtype == FLOAT:
            var = var.type(th.cuda.FloatTensor)
        else:
            raise ValueError('Unknown dtype')
    else:
        if dtype == INT:
            var = var.type(th.IntTensor)
        elif dtype == LONG:
            var = var.type(th.LongTensor)
        elif dtype == FLOAT:
            var = var.type(th.FloatTensor)
        else:
            raise ValueError('Unknown dtype')
    return var 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import IntTensor [as 别名]
def __init__(self, cfgfile, use_cuda=True):
        super(Darknet, self).__init__()
        self.use_cuda = use_cuda
        self.blocks = parse_cfg(cfgfile)
        self.models = self.create_network(self.blocks) # merge conv, bn,leaky
        self.loss_layers = self.getLossLayers()

        #self.width = int(self.blocks[0]['width'])
        #self.height = int(self.blocks[0]['height'])

        if len(self.loss_layers) > 0:
            last = len(self.loss_layers)-1
            self.anchors = self.loss_layers[last].anchors
            self.num_anchors = self.loss_layers[last].num_anchors
            self.anchor_step = self.loss_layers[last].anchor_step
            self.num_classes = self.loss_layers[last].num_classes

        # default format : major=0, minor=1
        self.header = torch.IntTensor([0,1,0,0])
        self.seen = 0 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import IntTensor [as 别名]
def tokenize(line, dict, tokenize=tokenize_line, add_if_not_exist=True,
                 consumer=None, append_eos=True, reverse_order=False):
        words = tokenize(line)
        if reverse_order:
            words = list(reversed(words))
        nwords = len(words)
        ids = torch.IntTensor(nwords + 1 if append_eos else nwords)

        for i, word in enumerate(words):
            if add_if_not_exist:
                idx = dict.add_symbol(word)
            else:
                idx = dict.index(word)
            if consumer is not None:
                consumer(word, idx)
            ids[i] = idx
        if append_eos:
            ids[nwords] = dict.eos_index
        return ids 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import IntTensor [as 别名]
def add(self, ref, pred):
        if not isinstance(ref, torch.IntTensor):
            raise TypeError('ref must be a torch.IntTensor (got {})'
                            .format(type(ref)))
        if not isinstance(pred, torch.IntTensor):
            raise TypeError('pred must be a torch.IntTensor(got {})'
                            .format(type(pred)))

        # don't match unknown words
        rref = ref.clone()
        assert not rref.lt(0).any()
        rref[rref.eq(self.unk)] = -999

        rref = rref.contiguous().view(-1)
        pred = pred.contiguous().view(-1)

        C.bleu_add(
            ctypes.byref(self.stat),
            ctypes.c_size_t(rref.size(0)),
            ctypes.c_void_p(rref.data_ptr()),
            ctypes.c_size_t(pred.size(0)),
            ctypes.c_void_p(pred.data_ptr()),
            ctypes.c_int(self.pad),
            ctypes.c_int(self.eos)) 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import IntTensor [as 别名]
def pytorch_one_hot(index_tensor, depth=0):
    """
    One-hot utility function for PyTorch.

    Args:
        index_tensor (torch.Tensor): The input to be one-hot.
        depth (int): The max. number to be one-hot encoded (size of last rank).

    Returns:
        torch.Tensor: The one-hot encoded equivalent of the input array.
    """
    if get_backend() == "pytorch":
        # Do converts.
        if isinstance(index_tensor, torch.FloatTensor):
            index_tensor = index_tensor.long()
        if isinstance(index_tensor, torch.IntTensor):
            index_tensor = index_tensor.long()

        out = torch.zeros(index_tensor.size() + torch.Size([depth]))
        dim = len(index_tensor.size())
        index = index_tensor.unsqueeze(-1)
        return out.scatter_(dim, index, 1) 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import IntTensor [as 别名]
def decode(self, emissions):
        B, T, N = emissions.size()
        hypos = []
        if self.asg_transitions is None:
            transitions = torch.FloatTensor(N, N).zero_()
        else:
            transitions = torch.FloatTensor(self.asg_transitions).view(N, N)
        viterbi_path = torch.IntTensor(B, T)
        workspace = torch.ByteTensor(CpuViterbiPath.get_workspace_size(B, T, N))
        CpuViterbiPath.compute(
            B,
            T,
            N,
            get_data_ptr_as_bytes(emissions),
            get_data_ptr_as_bytes(transitions),
            get_data_ptr_as_bytes(viterbi_path),
            get_data_ptr_as_bytes(workspace),
        )
        return [
            [{"tokens": self.get_tokens(viterbi_path[b].tolist()), "score": 0}]
            for b in range(B)
        ] 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import IntTensor [as 别名]
def parse_alignment(line):
    """
    Parses a single line from the alingment file.

    Args:
        line (str): String containing the alignment of the format:
            <src_idx_1>-<tgt_idx_1> <src_idx_2>-<tgt_idx_2> ..
            <src_idx_m>-<tgt_idx_m>. All indices are 0 indexed.

    Returns:
        torch.IntTensor: packed alignments of shape (2 * m).
    """
    alignments = line.strip().split()
    parsed_alignment = torch.IntTensor(2 * len(alignments))
    for idx, alignment in enumerate(alignments):
        src_idx, tgt_idx = alignment.split("-")
        parsed_alignment[2 * idx] = int(src_idx)
        parsed_alignment[2 * idx + 1] = int(tgt_idx)
    return parsed_alignment 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import IntTensor [as 别名]
def test_masked_global_attention(self):

        source_lengths = torch.IntTensor([7, 3, 5, 2])
        # illegal_weights_mask = torch.ByteTensor([
        #     [0, 0, 0, 0, 0, 0, 0],
        #     [0, 0, 0, 1, 1, 1, 1],
        #     [0, 0, 0, 0, 0, 1, 1],
        #     [0, 0, 1, 1, 1, 1, 1]])

        batch_size = source_lengths.size(0)
        dim = 20

        memory_bank = Variable(torch.randn(batch_size,
                                           source_lengths.max(), dim))
        hidden = Variable(torch.randn(batch_size, dim))

        attn = onmt.modules.GlobalAttention(dim)

        _, alignments = attn(hidden, memory_bank,
                             memory_lengths=source_lengths)
        # TODO: fix for pytorch 0.3
        # illegal_weights = alignments.masked_select(illegal_weights_mask)

        # self.assertEqual(0.0, illegal_weights.data.sum()) 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import IntTensor [as 别名]
def forward(self, forward_input):
        audio, spect = forward_input
        audio = self.start(audio)

        for i in range(self.n_layers):
            acts = fused_add_tanh_sigmoid_multiply(
                self.in_layers[i](audio),
                self.cond_layers[i](spect),
                torch.IntTensor([self.n_channels]))

            res_skip_acts = self.res_skip_layers[i](acts)
            if i < self.n_layers - 1:
                audio = res_skip_acts[:,:self.n_channels,:] + audio
                skip_acts = res_skip_acts[:,self.n_channels:,:]
            else:
                skip_acts = res_skip_acts

            if i == 0:
                output = skip_acts
            else:
                output = skip_acts + output
        return self.end(output)